【摘要】：土壤有機質空間分布預測方法研究對指導省域尺度下土壤有機質空間插值模型選取和精度優(yōu)化具有重要意義。以江西省為例,利用BP神經網絡模型與普通克里金結合的方法(BPNN-OK)、RBF神經網絡模型與普通克里金結合的方法(RBFNN-OK)以及普通克里金法(OK)3種方法,預測省域尺度下耕地表層(0~20 cm)土壤有機質的空間分布。16 109個土壤樣點隨機分成12 887個建模樣點,3 222個測試樣點。結果表明:在省域尺度下,BPNN-OK法、RBFNN-OK法較OK法在土壤有機質空間預測精度上有較大提升,三者的預測精度為BPNN-OKRBFNN-OKOK。BPNN-OK法對土壤有機質預測結果的均方根誤差、平均絕對誤差、平均相對誤差較OK法分別降低28.66%、30.71%、34.76%,RBFNN-OK法較OK法分別降低27.76%、29.74%、33.71%。在省域尺度下,神經網絡模型與普通克里金結合的方法能很好地捕捉土壤有機質的復雜空間變異關系。研究結果可指導江西省土壤有機質空間插值模型選取。
[Abstract]:The prediction method of soil organic matter spatial distribution is of great significance to guide the spatial interpolation model selection and precision optimization of soil organic matter in provincial scale. Taking Jiangxi Province as an example, using the BP neural network model and the ordinary Kriging method (BPNN-OK), there are three methods, the RBFNN-OK method and the (OK) method, which are combined with the common Kriging neural network model and the common Kriging neural network model, respectively. The spatial distribution of soil organic matter on the surface of cultivated land (0 ~ 20 cm) was predicted. 16 109 soil samples were randomly divided into 12 887 pattern sites and 3 222 test sites. The results showed that the precision of spatial prediction of soil organic matter by BPNN-OK method was much higher than that by OK method at the provincial scale. The accuracy of the three methods was the root mean square error and the average absolute error of BPNN-OKRBFNN-OKOK.BPNN-OK method for soil organic matter prediction. The average relative error was decreased by 28.660.71% and 34.76% respectively compared with the OK method. The RBFNN-OK method was 27.76% lower than the OK method (29.74%) and 33.71% lower than that of the OK method. On the provincial scale, the neural network model combined with the ordinary Kriging method can capture the complex spatial variability of soil organic matter. The results can guide the selection of spatial interpolation model of soil organic matter in Jiangxi Province.
【作者單位】： 江西農業(yè)大學國土資源與環(huán)境學院/江西省鄱陽湖流域農業(yè)資源與生態(tài)重點實驗室;南方糧油作物協同創(chuàng)新中心;
【基金】：國家自然科學基金項目(41361049) 江西省自然科學基金項目(20122BAB204012) 江西省贛鄱英才“555”領軍人才項目(201295)
【分類號】：S153.621

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